MAINT update tests and import for sklearn 1.4.1

examples/applications/porto_seguro_keras_under_sampling.py

@@ -151,7 +151,7 @@ def wrapper(*args, **kwds):
 # mini-batches.
 import tensorflow
 from sklearn.metrics import roc_auc_score
-from sklearn.utils import parse_version
+from sklearn.utils.fixes import parse_version
 
 tf_version = parse_version(tensorflow.__version__)
 

imblearn/_config.py

@@ -7,7 +7,7 @@
 from contextlib import contextmanager as contextmanager
 
 import sklearn
-from sklearn.utils import parse_version
+from sklearn.utils.fixes import parse_version
 
 sklearn_version = parse_version(sklearn.__version__)
 

imblearn/base.py

@@ -17,7 +17,7 @@
     from sklearn.base import _OneToOneFeatureMixin as OneToOneFeatureMixin
 
 from sklearn.preprocessing import label_binarize
-from sklearn.utils import parse_version
+from sklearn.utils.fixes import parse_version
 from sklearn.utils.multiclass import check_classification_targets
 
 from .utils import check_sampling_strategy, check_target_type

imblearn/ensemble/_bagging.py

@@ -16,7 +16,7 @@
 from sklearn.ensemble._base import _partition_estimators
 from sklearn.exceptions import NotFittedError
 from sklearn.tree import DecisionTreeClassifier
-from sklearn.utils import parse_version
+from sklearn.utils.fixes import parse_version
 from sklearn.utils.validation import check_is_fitted
 
 try:

imblearn/ensemble/_easy_ensemble.py

@@ -15,8 +15,8 @@
 from sklearn.ensemble._bagging import _parallel_decision_function
 from sklearn.ensemble._base import _partition_estimators
 from sklearn.exceptions import NotFittedError
-from sklearn.utils import parse_version
 from sklearn.utils._tags import _safe_tags
+from sklearn.utils.fixes import parse_version
 from sklearn.utils.validation import check_is_fitted
 
 try:

imblearn/ensemble/_forest.py

@@ -22,7 +22,8 @@
 )
 from sklearn.exceptions import DataConversionWarning
 from sklearn.tree import DecisionTreeClassifier
-from sklearn.utils import _safe_indexing, check_random_state, parse_version
+from sklearn.utils import _safe_indexing, check_random_state
+from sklearn.utils.fixes import parse_version
 from sklearn.utils.multiclass import type_of_target
 from sklearn.utils.validation import _check_sample_weight
 

imblearn/ensemble/_weight_boosting.py

@@ -8,7 +8,8 @@
 from sklearn.ensemble import AdaBoostClassifier
 from sklearn.ensemble._base import _set_random_states
 from sklearn.tree import DecisionTreeClassifier
-from sklearn.utils import _safe_indexing, parse_version
+from sklearn.utils import _safe_indexing
+from sklearn.utils.fixes import parse_version
 from sklearn.utils.validation import has_fit_parameter
 
 from ..base import _ParamsValidationMixin

imblearn/keras/tests/test_generator.py

@@ -70,7 +70,7 @@ def test_balanced_batch_generator_class(data, sampler, sample_weight):
         batch_size=10,
         random_state=42,
     )
-    model.fit_generator(generator=training_generator, epochs=10)
+    model.fit(training_generator, epochs=10)
 
 
 @pytest.mark.parametrize("keep_sparse", [True, False])
@@ -122,8 +122,8 @@ def test_balanced_batch_generator_function(data, sampler, sample_weight):
         batch_size=10,
         random_state=42,
     )
-    model.fit_generator(
-        generator=training_generator,
+    model.fit(
+        training_generator,
         steps_per_epoch=steps_per_epoch,
         epochs=10,
     )

imblearn/tests/test_pipeline.py

@@ -410,7 +410,7 @@ def test_fit_predict_on_pipeline_without_fit_predict():
     scaler = StandardScaler()
     pca = PCA(svd_solver="full")
     pipe = Pipeline([("scaler", scaler), ("pca", pca)])
-    error_regex = "'PCA' object has no attribute 'fit_predict'"
+    error_regex = "has no attribute 'fit_predict'"
     with raises(AttributeError, match=error_regex):
         getattr(pipe, "fit_predict")
 
@@ -1219,7 +1219,7 @@ def test_score_samples_on_pipeline_without_score_samples():
     pipe.fit(X, y)
     with pytest.raises(
         AttributeError,
-        match="'LogisticRegression' object has no attribute 'score_samples'",
+        match="has no attribute 'score_samples'",
     ):
         pipe.score_samples(X)
 

imblearn/utils/_available_if.py

@@ -7,7 +7,7 @@
 from types import MethodType
 
 import sklearn
-from sklearn.utils import parse_version
+from sklearn.utils.fixes import parse_version
 
 sklearn_version = parse_version(sklearn.__version__)